Towards Improved Phishing Website Detection: Heuristic-Based Approaches vs. Machine Learning

Phishing is widely acknowledged as one of the most insidious types of social engineering attacks. Despite substantial efforts to combat this issue, it continues to evolve in sophistication, resulting in increasing financial losses. Historically, countering phishing involved a blend of human vigilance and software-based detection mechanisms, primarily relying on list-based strategies. However, with the advent of advanced data science, innovative phishing detection techniques utilizing Machine Learning models have emerged and garnered significant research attention. This study aims to comprehensively compare the effectiveness of traditional heuristic-based and modern Machine Learning classification models, while addressing challenges associated with their efficiency. Experimental results involving the Random Forest classifier, although requiring slightly more computational power, demonstrated a substantial increase in detection accuracy (57.2% higher) and a remarkable reduction in testing time (11.28 seconds faster vs 0.01s) when compared to heuristics using the same input data.